
from __future__ import annotations

import inspect
import unittest

from transformers import ViTConfig
from transformers.testing_utils import require_tf, require_vision, slow
from transformers.utils import cached_property, is_tf_available, is_vision_available

from ...test_configuration_common import ConfigTester
from ...test_modeling_tf_common import TFModelTesterMixin, floats_tensor, ids_tensor
from ...test_pipeline_mixin import PipelineTesterMixin


if is_tf_available():
		import tensorflow as tf

		from transformers import TFViTForImageClassification, TFViTModel


if is_vision_available():
		from PIL import Image

		from transformers import ViTImageProcessor




class 					lowerCamelCase__				:




	'''simple docstring'''




	def __init__(   self						:str	,  a						:Any	,  a						:Optional[Any]=1_3	,  a						:List[str]=3_0	,  a						:Optional[Any]=2	,  a						:Tuple=3	,  a						:Optional[int]=True	,  a						:Dict=True	,  a						:Any=3_2	,  a						:Optional[int]=2	,  a						:str=4	,  a						:str=3_7	,  a						:Any="gelu"	,  a						:Union[str, Any]=0.1	,  a						:Dict=0.1	,  a						:Tuple=1_0	,  a						:Optional[int]=0.02	,  a						:Union[str, Any]=3	,  a						:List[str]=None	,  )						->					List[Any]:
								__UpperCamelCase      :  int       =   parent
								__UpperCamelCase      :  Any       =   batch_size
								__UpperCamelCase      :  Optional[Any]       =   image_size
								__UpperCamelCase      :  Any       =   patch_size
								__UpperCamelCase      :  Dict       =   num_channels
								__UpperCamelCase      :  Optional[int]       =   is_training
								__UpperCamelCase      :  Dict       =   use_labels
								__UpperCamelCase      :  Optional[Any]       =   hidden_size
								__UpperCamelCase      :  str       =   num_hidden_layers
								__UpperCamelCase      :  Tuple       =   num_attention_heads
								__UpperCamelCase      :  Dict       =   intermediate_size
								__UpperCamelCase      :  Any       =   hidden_act
								__UpperCamelCase      :  Optional[int]       =   hidden_dropout_prob
								__UpperCamelCase      :  Optional[int]       =   attention_probs_dropout_prob
								__UpperCamelCase      :  int       =   type_sequence_label_size
								__UpperCamelCase      :  List[str]       =   initializer_range
								__UpperCamelCase      :  int       =   scope

								# in ViT, the seq length equals the number of patches + 1 (we add 1 for the [CLS] token)
								__UpperCamelCase      :  int       =   (image_size // patch_size) ** 2
								__UpperCamelCase      :  Dict       =   num_patches + 1







	def   _lowerCamelCase							(   self						:Any		)						->					Optional[Any]:
								__UpperCamelCase      :  List[str]       =   floats_tensor([self.batch_size, self.num_channels, self.image_size, self.image_size]		)

								__UpperCamelCase      :  Tuple       =   None
								if self.use_labels:
															__UpperCamelCase      :  Dict       =   ids_tensor([self.batch_size]	,  self.type_sequence_label_size		)

								__UpperCamelCase      :  Optional[Any]       =   self.get_config()

								return config, pixel_values, labels







	def   _lowerCamelCase							(   self						:str		)						->					Optional[Any]:
								return ViTConfig(
								    image_size=self.image_size	,  patch_size=self.patch_size	,  num_channels=self.num_channels	,  hidden_size=self.hidden_size	,  num_hidden_layers=self.num_hidden_layers	,  num_attention_heads=self.num_attention_heads	,  intermediate_size=self.intermediate_size	,  hidden_act=self.hidden_act	,  hidden_dropout_prob=self.hidden_dropout_prob	,  attention_probs_dropout_prob=self.attention_probs_dropout_prob	,  is_decoder=a	,  initializer_range=self.initializer_range	,  )







	def   _lowerCamelCase							(   self						:Tuple	,  a						:List[Any]	,  a						:Dict	,  a						:Optional[int]		)						->					Tuple:
								__UpperCamelCase      :  Union[str, Any]       =   TFViTModel(config=a		)
								__UpperCamelCase      :  Optional[int]       =   model(a	,  training=a		)
								self.parent.assertEqual(result.last_hidden_state.shape	,  (self.batch_size, self.seq_length, self.hidden_size)		)

								# Test with an image with different size than the one specified in config.
								__UpperCamelCase      :  Union[str, Any]       =   self.image_size // 2
								__UpperCamelCase      :  Dict       =   pixel_values[:, :, :image_size, :image_size]
								__UpperCamelCase      :  Tuple       =   model(a	,  interpolate_pos_encoding=a	,  training=a		)
								__UpperCamelCase      :  Tuple       =   (image_size // self.patch_size) ** 2 + 1
								self.parent.assertEqual(result.last_hidden_state.shape	,  (self.batch_size, seq_length, self.hidden_size)		)







	def   _lowerCamelCase							(   self						:int	,  a						:List[str]	,  a						:Tuple	,  a						:str		)						->					Any:
								__UpperCamelCase      :  str       =   self.type_sequence_label_size
								__UpperCamelCase      :  Dict       =   TFViTForImageClassification(a		)
								__UpperCamelCase      :  int       =   model(a	,  labels=a	,  training=a		)
								self.parent.assertEqual(result.logits.shape	,  (self.batch_size, self.type_sequence_label_size)		)

								# Test with an image with different size than the one specified in config.
								__UpperCamelCase      :  Optional[int]       =   self.image_size // 2
								__UpperCamelCase      :  List[Any]       =   pixel_values[:, :, :image_size, :image_size]
								__UpperCamelCase      :  Any       =   model(a	,  interpolate_pos_encoding=a	,  training=a		)
								self.parent.assertEqual(result.logits.shape	,  (self.batch_size, self.type_sequence_label_size)		)

								# test greyscale images
								__UpperCamelCase      :  List[Any]       =   1
								__UpperCamelCase      :  Dict       =   TFViTForImageClassification(a		)
								__UpperCamelCase      :  str       =   floats_tensor([self.batch_size, 1, self.image_size, self.image_size]		)
								__UpperCamelCase      :  Union[str, Any]       =   model(a		)
								self.parent.assertEqual(result.logits.shape	,  (self.batch_size, self.type_sequence_label_size)		)







	def   _lowerCamelCase							(   self						:Optional[int]		)						->					Optional[int]:
								__UpperCamelCase      :  List[str]       =   self.prepare_config_and_inputs()
								__UpperCamelCase  ,		__UpperCamelCase  ,		__UpperCamelCase      :  Any       =   config_and_inputs
								__UpperCamelCase      :  Dict       =   {"pixel_values": pixel_values}
								return config, inputs_dict




@require_tf
class 					lowerCamelCase__				(							__lowercase	,		__lowercase	,		unittest.TestCase):




	'''simple docstring'''




	_A								=	(TFViTModel, TFViTForImageClassification) if is_tf_available() else ()
	_A								=	(
	    {'feature-extraction': TFViTModel, 'image-classification': TFViTForImageClassification}
	    if is_tf_available()
	    else {}
	)

	_A								=	False
	_A								=	False
	_A								=	False







	def   _lowerCamelCase							(   self						:int		)						->					Optional[Any]:
								__UpperCamelCase      :  List[str]       =   TFViTModelTester(self		)
								__UpperCamelCase      :  Any       =   ConfigTester(self	,  config_class=a	,  has_text_modality=a	,  hidden_size=3_7		)







	def   _lowerCamelCase							(   self						:Optional[int]		)						->					Optional[Any]:
								self.config_tester.run_common_tests()







	@unittest.skip(reason="ViT does not use inputs_embeds"		)
	def   _lowerCamelCase							(   self						:List[Any]		)						->					List[Any]:
								pass







	@unittest.skip(reason="ViT does not use inputs_embeds"		)
	def   _lowerCamelCase							(   self						:Any		)						->					Any:
								pass







	def   _lowerCamelCase							(   self						:Optional[Any]		)						->					List[str]:
								__UpperCamelCase  ,		__UpperCamelCase      :  Union[str, Any]       =   self.model_tester.prepare_config_and_inputs_for_common()

								for model_class in self.all_model_classes:
															__UpperCamelCase      :  Any       =   model_class(a		)
															self.assertIsInstance(model.get_input_embeddings()	,  (tf.keras.layers.Layer)		)
															__UpperCamelCase      :  Union[str, Any]       =   model.get_output_embeddings()
															self.assertTrue(x is None or isinstance(a	,  tf.keras.layers.Layer		)		)







	def   _lowerCamelCase							(   self						:Optional[int]		)						->					Dict:
								__UpperCamelCase  ,		__UpperCamelCase      :  Optional[int]       =   self.model_tester.prepare_config_and_inputs_for_common()

								for model_class in self.all_model_classes:
															__UpperCamelCase      :  Union[str, Any]       =   model_class(a		)
															__UpperCamelCase      :  Optional[Any]       =   inspect.signature(model.call		)
															# signature.parameters is an OrderedDict => so arg_names order is deterministic
															__UpperCamelCase      :  Optional[Any]       =   [*signature.parameters.keys()]

															__UpperCamelCase      :  str       =   ["pixel_values"]
															self.assertListEqual(arg_names[:1]	,  a		)







	def   _lowerCamelCase							(   self						:Dict		)						->					Any:
								__UpperCamelCase      :  Optional[Any]       =   self.model_tester.prepare_config_and_inputs()
								self.model_tester.create_and_check_model(*a		)







	def   _lowerCamelCase							(   self						:Optional[int]		)						->					Optional[int]:
								__UpperCamelCase      :  Optional[Any]       =   self.model_tester.prepare_config_and_inputs()
								self.model_tester.create_and_check_for_image_classification(*a		)







	@slow
	def   _lowerCamelCase							(   self						:Optional[Any]		)						->					List[Any]:
								__UpperCamelCase      :  int       =   TFViTModel.from_pretrained("google/vit-base-patch16-224"		)
								self.assertIsNotNone(a		)




def       _SCREAMING_SNAKE_CASE   (						)   ->       List[str]:






							'''simple docstring'''





							__UpperCamelCase      :  Tuple       =   Image.open("./tests/fixtures/tests_samples/COCO/000000039769.png")
							return image




@require_tf
@require_vision
class 					lowerCamelCase__				(							unittest.TestCase):




	'''simple docstring'''




	@cached_property
	def   _lowerCamelCase							(   self						:str		)						->					str:
								return ViTImageProcessor.from_pretrained("google/vit-base-patch16-224"		) if is_vision_available() else None







	@slow
	def   _lowerCamelCase							(   self						:Any		)						->					Union[str, Any]:
								__UpperCamelCase      :  Optional[Any]       =   TFViTForImageClassification.from_pretrained("google/vit-base-patch16-224"		)

								__UpperCamelCase      :  List[str]       =   self.default_image_processor
								__UpperCamelCase      :  Optional[Any]       =   prepare_img()
								__UpperCamelCase      :  List[str]       =   image_processor(images=a	,  return_tensors="tf"		)

								# forward pass
								__UpperCamelCase      :  Optional[int]       =   model(**a		)

								# verify the logits
								__UpperCamelCase      :  Optional[int]       =   tf.TensorShape((1, 1_0_0_0)		)
								self.assertEqual(outputs.logits.shape	,  a		)

								__UpperCamelCase      :  List[Any]       =   tf.constant([-0.2744, 0.8215, -0.0836]		)

								tf.debugging.assert_near(outputs.logits[0, :3]	,  a	,  atol=1E-4		)

import logging
import os
from typing import List, TextIO, Union

from conllu import parse_incr
from utils_ner import InputExample, Split, TokenClassificationTask


lowercase    :   Tuple						=			logging.getLogger(__name__)




class 					lowerCamelCase__				(							__lowercase):




	'''simple docstring'''




	def __init__(   self						:Dict	,  a						:Union[str, Any]=-1		)						->					List[str]:
								# in NER datasets, the last column is usually reserved for NER label
								__UpperCamelCase      :  str       =   label_idx







	def   _lowerCamelCase							(   self						:str	,  a						:Tuple	,  a						:Union[Split, str]		)						->					List[InputExample]:
								if isinstance(a	,  a		):
															__UpperCamelCase      :  Dict       =   mode.value
								__UpperCamelCase      :  Union[str, Any]       =   os.path.join(a	,  f'{mode}.txt'		)
								__UpperCamelCase      :  Any       =   1
								__UpperCamelCase      :  List[str]       =   []
								with open(a	,  encoding="utf-8"		) as f:
															__UpperCamelCase      :  Tuple       =   []
															__UpperCamelCase      :  Dict       =   []
															for line in f:
																						if line.startswith("-DOCSTART-"		) or line == "" or line == "\n":
																													if words:
																																				examples.append(InputExample(guid=f'{mode}-{guid_index}'	,  words=a	,  labels=a		)		)
																																				guid_index += 1
																																				__UpperCamelCase      :  Dict       =   []
																																				__UpperCamelCase      :  List[str]       =   []
																						else:
																													__UpperCamelCase      :  Union[str, Any]       =   line.split(" "		)
																													words.append(splits[0]		)
																													if len(a		) > 1:
																																				labels.append(splits[self.label_idx].replace("\n"	,  ""		)		)
																													else:
																																				# Examples could have no label for mode = "test"
																																				labels.append("O"		)
															if words:
																						examples.append(InputExample(guid=f'{mode}-{guid_index}'	,  words=a	,  labels=a		)		)
								return examples







	def   _lowerCamelCase							(   self						:int	,  a						:TextIO	,  a						:TextIO	,  a						:List		)						->					Optional[Any]:
								__UpperCamelCase      :  Tuple       =   0
								for line in test_input_reader:
															if line.startswith("-DOCSTART-"		) or line == "" or line == "\n":
																						writer.write(a		)
																						if not preds_list[example_id]:
																													example_id += 1
															elif preds_list[example_id]:
																						__UpperCamelCase      :  Union[str, Any]       =   line.split()[0] + " " + preds_list[example_id].pop(0		) + "\n"
																						writer.write(a		)
															else:
																						logger.warning("Maximum sequence length exceeded: No prediction for '%s'."	,  line.split()[0]		)







	def   _lowerCamelCase							(   self						:Tuple	,  a						:str		)						->					List[str]:
								if path:
															with open(a	,  "r"		) as f:
																						__UpperCamelCase      :  List[str]       =   f.read().splitlines()
															if "O" not in labels:
																						__UpperCamelCase      :  Any       =   ["O"] + labels
															return labels
								else:
															return ["O", "B-MISC", "I-MISC", "B-PER", "I-PER", "B-ORG", "I-ORG", "B-LOC", "I-LOC"]




class 					lowerCamelCase__				(							__lowercase):




	'''simple docstring'''




	def __init__(   self						:List[str]		)						->					int:
								# in CONLL2003 dataset chunk column is second-to-last
								super().__init__(label_idx=-2		)







	def   _lowerCamelCase							(   self						:List[str]	,  a						:str		)						->					List[str]:
								if path:
															with open(a	,  "r"		) as f:
																						__UpperCamelCase      :  Optional[Any]       =   f.read().splitlines()
															if "O" not in labels:
																						__UpperCamelCase      :  int       =   ["O"] + labels
															return labels
								else:
															return [
															    "O",
															    "B-ADVP",
															    "B-INTJ",
															    "B-LST",
															    "B-PRT",
															    "B-NP",
															    "B-SBAR",
															    "B-VP",
															    "B-ADJP",
															    "B-CONJP",
															    "B-PP",
															    "I-ADVP",
															    "I-INTJ",
															    "I-LST",
															    "I-PRT",
															    "I-NP",
															    "I-SBAR",
															    "I-VP",
															    "I-ADJP",
															    "I-CONJP",
															    "I-PP",
															]




class 					lowerCamelCase__				(							__lowercase):




	'''simple docstring'''




	def   _lowerCamelCase							(   self						:List[str]	,  a						:Union[str, Any]	,  a						:Union[Split, str]		)						->					List[InputExample]:
								if isinstance(a	,  a		):
															__UpperCamelCase      :  Optional[Any]       =   mode.value
								__UpperCamelCase      :  List[str]       =   os.path.join(a	,  f'{mode}.txt'		)
								__UpperCamelCase      :  Dict       =   1
								__UpperCamelCase      :  List[str]       =   []

								with open(a	,  encoding="utf-8"		) as f:
															for sentence in parse_incr(a		):
																						__UpperCamelCase      :  Optional[int]       =   []
																						__UpperCamelCase      :  int       =   []
																						for token in sentence:
																													words.append(token["form"]		)
																													labels.append(token["upos"]		)
																						assert len(a		) == len(a		)
																						if words:
																													examples.append(InputExample(guid=f'{mode}-{guid_index}'	,  words=a	,  labels=a		)		)
																													guid_index += 1
								return examples







	def   _lowerCamelCase							(   self						:List[Any]	,  a						:TextIO	,  a						:TextIO	,  a						:List		)						->					str:
								__UpperCamelCase      :  List[Any]       =   0
								for sentence in parse_incr(a		):
															__UpperCamelCase      :  Tuple       =   preds_list[example_id]
															__UpperCamelCase      :  Dict       =   ""
															for token in sentence:
																						out += f'{token["form"]} ({token["upos"]}|{s_p.pop(0		)}) '
															out += "\n"
															writer.write(a		)
															example_id += 1







	def   _lowerCamelCase							(   self						:List[str]	,  a						:str		)						->					List[str]:
								if path:
															with open(a	,  "r"		) as f:
																						return f.read().splitlines()
								else:
															return [
															    "ADJ",
															    "ADP",
															    "ADV",
															    "AUX",
															    "CCONJ",
															    "DET",
															    "INTJ",
															    "NOUN",
															    "NUM",
															    "PART",
															    "PRON",
															    "PROPN",
															    "PUNCT",
															    "SCONJ",
															    "SYM",
															    "VERB",
															    "X",
															]